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revert(http): 改回串行数据获取

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回退并行获取逻辑，恢复简单的串行循环：
- 移除 ThreadPoolExecutor 并行代码
- 移除 concurrent.futures 导入
- 保持简单的 for 循环串行获取
This commit is contained in:
aszerW
2026-06-03 00:09:29 +08:00
parent e29f57749d
commit a2b4289080
1 changed files with 18 additions and 41 deletions
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59
rotation/simple_rotation.py
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@@ -21,7 +21,6 @@ import pandas as pd
from pathlib import Path
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Tuple
from concurrent.futures import ThreadPoolExecutor, as_completed
PROJECT_ROOT = Path(__file__).parent.parent
sys.path.insert(0, str(PROJECT_ROOT))
@@ -368,54 +367,32 @@ class SimpleRotationStrategy:
self.trading_calendar: Optional[pd.DatetimeIndex] = None
def _preload_data(self):
"""Preload all historical data (parallel fetching)"""
"""Preload all historical data"""
start_date = self.config.backtest.start_date
end_date = self.config.backtest.end_date or datetime.now().strftime('%Y-%m-%d')
preload_start = (pd.Timestamp(start_date) - timedelta(days=self.n_days * 2)).strftime('%Y-%m-%d')
print(f"\n[1/4] Preloading signal sources (index raw) [{len(self.signal_codes)} codes, parallel]...")
# Parallel fetch signal sources
with ThreadPoolExecutor(max_workers=8) as executor:
futures = {executor.submit(self.data_cache.preload, code, preload_start, end_date, 'raw'): code for code in self.signal_codes}
for future in as_completed(futures):
code = futures[future]
try:
df = future.result()
if df is not None:
self.index_data[code] = df
except Exception as e:
print(f" x {code}: {e}")
print("\n[1/4] Preloading signal sources (index raw)...")
for code in self.signal_codes:
df = self.data_cache.preload(code, preload_start, end_date, adj='raw')
if df is not None:
self.index_data[code] = df
print(f"\n Signal: {len(self.index_data)}/{len(self.signal_codes)} OK")
print(f"\n[2/4] Preloading trade sources (ETF hfq) [{len(set(self.signal_to_trade.values()))} codes, parallel]...")
print("\n[2/4] Preloading trade sources (ETF hfq)...")
trade_codes = set(self.signal_to_trade.values())
# Determine adj for each trade code
trade_adj_map = {}
for code in trade_codes:
is_bond = any(a.trade_source == code and a.group == 'BOND' for a in self.config.asset_pools.assets.values())
trade_adj_map[code] = 'raw' if is_bond else 'hfq'
# Parallel fetch trade sources
with ThreadPoolExecutor(max_workers=8) as executor:
futures = {executor.submit(self.data_cache.preload, code, preload_start, end_date, trade_adj_map[code]): code for code in trade_codes}
for future in as_completed(futures):
code = futures[future]
try:
df = future.result()
if df is not None:
self.etf_data[code] = df
except Exception as e:
print(f" x {code}: {e}")
# Parallel fetch premium data
with ThreadPoolExecutor(max_workers=8) as executor:
futures = {executor.submit(self.data_cache.preload_premium, code, end_date): code for code in trade_codes}
for future in as_completed(futures):
code = futures[future]
try:
future.result()
except Exception:
pass
is_bond = any(
a.trade_source == code and a.group == 'BOND'
for a in self.config.asset_pools.assets.values()
)
adj = 'raw' if is_bond else 'hfq'
df = self.data_cache.preload(code, preload_start, end_date, adj=adj)
if df is not None:
self.etf_data[code] = df
# Load premium data cache for all ETF trade codes
for code in trade_codes:
self.data_cache.preload_premium(code, end_date=end_date)
print(f"\n Trade: {len(self.etf_data)}/{len(trade_codes)} OK, premium: {len(self.data_cache.premium_data)} loaded")
# Load benchmark